Abstract Coastal hazard is rarely driven by only one source, as exemplified by the compound flooding from Hurricane Harvey in Galveston Bay in 2017. A 3D creek-to-ocean model is developed to explicitly resolve, without grid nesting, the marine (combination of atmospheric forcing and tides), fluvial and pluvial extremes for this extreme event. We first thoroughly assess the model skills using all available observations in the Galveston Bay region, including High Water Marks (HWMs) and field estimates of maximum inundation extent in the watershed. Subject to uncertainties in the river flows, atmospheric forcing, initial condition of salinity and temperature, and digital elevation model of bathymetry-topography, the model is shown to generally exhibit good skills for predicting inundation and compound surges, with a hit rate for inundation extent of 0.92, average mean-absolute-errors of 0.65 m for HWMs, 1.7 psu for salinity, and 1.4 °C for temperature. We then apply the model to quantify the individual contributions from the three major forcings (ocean, river and precipitation). Comparison of results (in the form of ‘compound ratio’) from the simulations with three factors being applied individually with those from the baseline simulation with all factors included in a single model reveals the nonlinear compounding effects in most of the areas in Galveston Bay, and indicates that the compound flooding problems are best simulated using a single model that integrates across all factors because the interactions among processes are very complex and highly nonlinear; in other words, summing up the results from individual forcings would lead to large errors. For example, the hydrodynamic model results forced by river inflows at boundary and oceanic and atmospheric forcings, without explicitly accounting for the direct precipitation in the coastal watersheds, would severely underestimate the resultant flow and surge by up to 90%. ‘Regions of dominance’ are also identified for each forcing factor from the sensitivity results. These concepts are applicable to other compound flooding studies as well.

中文翻译：

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哈维飓风期间加尔维斯顿湾周围极端洪水的复合因素

摘要 沿海灾害很少由单一来源驱动，例如 2017 年加尔维斯顿湾哈维飓风的复合洪水。开发了 3D 小溪到海洋模型，无需网格嵌套，明确解决海洋（大气的组合）强迫和潮汐），该极端事件的河流和雨洪极端事件。我们首先使用加尔维斯顿湾地区的所有可用观测结果彻底评估模型技能，包括高水位线 (HWM) 和流域最大淹没范围的实地估计。受河流流量、大气强迫、盐度和温度的初始条件以及测深地形数字高程模型的不确定性的影响，该模型通常显示出预测洪水和复合浪涌的良好技能，淹没范围的命中率为 0.92，HWM 的平均平均绝对误差为 0.65 m，盐度为 1.7 psu，温度为 1.4 °C。然后我们应用该模型来量化来自三个主要强迫（海洋、河流和降水）的个体贡献。将单独应用三个因子的模拟结果（以“复合比”的形式）与所有因子包含在单个模型中的基线模拟的结果进行比较，揭示了加尔维斯顿湾大部分地区的非线性复合效应，并表明复合洪水问题最好使用整合所有因素的单一模型进行模拟，因为过程之间的相互作用非常复杂且高度非线性；换句话说，将单个强迫的结果相加会导致很大的误差。例如，边界处河流流入以及海洋和大气强迫的水动力模型结果，如果没有明确考虑沿海流域的直接降水，将严重低估合成流量和高达 90% 的激增。敏感度结果中的每个强迫因素也确定了“优势区域”。这些概念也适用于其他复合驱研究。